USDA
Engineering Next-Generation Agricultural Data Platforms
Challenge
USDA NASS operates a complex ecosystem supporting nationwide surveys, statistical modeling, and data dissemination. Legacy systems built on fragmented technology stacks limited scalability, slowed development, and constrained adoption of automation and emerging technologies. Modernization had to occur without disrupting active survey operations across 12 regional offices.
Solution
WINTrio transformed the ecosystem into a modular, cloud-ready platform enabling advanced data operations and emerging technologies.
- Enterprise Platform Modernization: Built a microservices-based architecture with reusable services for authentication, data access, logging, and document management.
- AI/ML-Enabled Processing: Introduced machine learning models to automate commodity classification and accelerate data workflows.
- AI-Ready Data Architecture: Established standardized metadata services and API-driven pipelines to support analytics and automation.
- Automated Data Pipelines: Implemented high-volume ingestion and processing pipelines to reduce manual effort and improve scalability.
Outcomes
- Achieved approximately 40% enterprise code reuse through platform standardization
- Reduced manual data preparation by approximately 50%
- Improved data processing speed by up to 70%
- Reduced manual classification effort by up to 76% using machine learning
- Delivered mobile data collection capabilities ahead of schedule
- Enabled scalable, AI-ready architecture for future analytics and automation
Walmart
AI/ML Forecasting & Global Technology Modernization
Challenge
Walmart operates one of the world’s largest retail ecosystems, supporting 11,000 stores, millions of SKUs, and high-velocity global supply chains. Dispersed data environments and legacy application constraints limited forecasting accuracy, slowed decision-making, and increased operational complexity. Modernization had to occur without disrupting real-time retail operations at global scale.
Solution
WINTrio partnered with Walmart’s global technology teams to modernize enterprise systems and enable AI-driven decision-making.
- AI/ML Forecasting Models: Developed predictive analytics to optimize demand forecasting, inventory planning, and supply chain execution.
- Multi-Cloud Modernization: Supported migration and optimization across AWS, Azure, and Google Cloud, improving scalability and cost efficiency.
- DevOps & CI/CD Transformation: Implemented automated pipelines to accelerate deployment and improve release reliability.
- Enterprise Monitoring & Performance Engineering: Established real-time monitoring frameworks to ensure system stability across high-volume workloads.
Outcomes
- Improved demand forecasting accuracy and supply chain predictability
- Reduced stockouts and overstock through data-driven inventory optimization
- Accelerated cloud adoption with optimized infrastructure costs
- Reduced deployment cycles by up to 50% through CI/CD automation
- Improved system resilience and uptime across high-volume retail platforms
- Enabled real-time operational visibility and data-driven decision-making
